August | 2016
In one of my recent papers, titled “Belling Schrödinger’s cat“, I had picked the analogy of Schrödinger’s thought experiment around how it was not possible to conceive if the cat was alive or not, and how in a similar fashion in business we have ambiguity in data points, that could potentially be used to extract superior decisions.
Examples of ambiguity arising out of a decision process can be seen in the following situations:
These are scenarios that we commonly encounter, however today, advancements in technology is helping enterprises turn a different chapter. With today’s smart detection techniques, we have seen implementation moving from a rule-based regime to a probabilistic prediction of anomalies – as I spoke about earlier here.
In the above mentioned business scenarios, building an anomaly detection model that does not have a meaningful approach to handling the ‘unproven suspects‘ or ‘greys‘ could impact the predictive accuracy. We have seen that these ‘greys’ are typically handled in one of the following ways:
All these options lead to loss of vital information and hence inferior decision making. To overcome this, we explored if there is a way to leverage the “two-state” nature of the grey set to build a better anomaly detection model, compared to the above-mentioned options. Essentially what we sought to do was to run a series of iterations and assume that the ‘grey’ data points could either be ‘blacks’ or ‘whites’ in each of these iterations and build this into our prediction model and then consolidate the results. The individual predictors could be any of the traditional classifiers.
The approach was implemented on three different datasets across three very different business domains. All three datasets contained a small number of known frauds and greys (under 1% each) and the remaining were normal / clean records.
The key findings from the results are as follows:
As we deploy this approach in solving other compelling business problems, we are achieving more flawless detection models – with false positive rates dropping further and greater process velocity strengthening the organization’s risk climate.
R. Guha heads Corporate Business Development at Wipro Technologies and is focused on two strategic themes at the intersection of technology and business: cybersecurity framed from a business risk perspective and how to leverage machine learning for business transformation.
Guha is currently leading the development and deployment of Apollo, an anomaly detection platform that seeks to mitigate risk and improve process velocity through smarter detection.
© 2021 Wipro Limited |
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© 2021 Wipro Limited |
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